Fuzzy Bayesian networks for prognostics and health management

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Montana State University - Bozeman, College of Engineering


In systems diagnostics it is often difficult to define test requirements and acceptance thresholds for these tests. A technique that can be used to alleviate this problem is to use fuzzy membership values to represent the degree of membership of a particular test outcome. Bayesian networks are commonly used tools for diagnostics and prognostics; however, they do not accept inputs of fuzzy values. To remedy this we present a novel application of fuzzy Bayesian networks in the context of prognostics and health management. These fuzzy Bayesian networks can use fuzzy values as evidence and can produce fuzzy membership values for diagnoses that can be used to represent component level degradation within a system. We developed a novel execution ordering algorithm used in evaluating the fuzzy Bayesian networks, as well as a method for integrating fuzzy evidence with inferred fuzzy state information. We use three different diagnostic networks to illustrate the feasibility of fuzzy Bayesian networks in the context of prognostics. We are able to use this technique to determine battery capacity degradation as well as component degradation in two test circuits.




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